Geospatial is a term that is used widely to describe the combination of spatial software and analytical methods with terrestrial or geographic datasets. Geospatial data identifies the geographic location and characteristics of natural or constructed features and boundaries on the earth. This information is gathered from remote sensing, mapping, surveying and other technologies. Geospatial systems capture, store, manage, integrate, display, analyze and otherwise assist in the interpretation of this data in its entire context for better decision-making. A geographic location optionally is represented as a point, line, area or volume, and optionally is represented in either two-dimensions or three-dimensions.
Unfortunately, experts in data collection are typically separate and distinct from experts in data processing. In data collection, maps and other spatial information are combined to form a larger spatial dataset. For example, addresses are superimposed on a map or two maps are coupled to form a larger map. This data is then stored in a structure that is suitable for processing thereof. Once a dataset is formed, it becomes available for analysis. Experts in geographic data analysis develop processes that are designed to operate with the data structure to extract from the dataset “useful” information. For each problem that is encountered, an expert is consulted to design a process that allows the problem to be better analysed, avoided, or solved. Since the process is designed for a given dataset and for solving a given solution, the process designer can ensure that the process functions within specifications and, once designed, the process can be executed on the dataset in a repeatable fashion.
As is true of information in general, the volume of available geographic data is expanding continuously, thereby providing process developers with new opportunities to develop improved processes that enable decision makers to make better and more informed decisions. That said, most process developers are well versed in specific datasets thereby limiting the available datasets for their processes. Furthermore, many organizations already have access to specific datasets, and as such, tend only to work with process developers that are already versed in processing those datasets. Despite this tendency, some processes are executable on datasets other than the ones for which the process was developed initially. In some cases, the same process may yield different geospatial information depending on the dataset that is being analyzed. That is to say, the geospatial information may be biased in some way as a result of the way each dataset was created, such as for instance either during collection of the geographic data itself or in the way the structure of the dataset was defined. Similarly, different processes executed on the same dataset may yield different geospatial information depending upon the particular bias that is built into each different process.
Currently, there is not a reliable method for evaluating a ranking of geospatial processes and datasets. In effect, a process developer is selected and employed for processing a particular dataset or for processing a convenient dataset. In either case, a “best” result may not ensue.
It would be beneficial to provide a method and system that solves at least some of the above-mentioned problems.